Overview

Dataset statistics

Number of variables21
Number of observations21613
Missing cells5
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.5 MiB
Average record size in memory168.0 B

Variable types

Numeric17
Categorical4

Warnings

date has a high cardinality: 372 distinct values High cardinality
sqft_basement has 13126 (60.7%) zeros Zeros
yr_renovated has 20699 (95.8%) zeros Zeros

Reproduction

Analysis started2021-06-19 18:21:09.488234
Analysis finished2021-06-19 18:21:46.932949
Duration37.44 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

id
Real number (ℝ≥0)

Distinct21436
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4580301521
Minimum1000102
Maximum9900000190
Zeros0
Zeros (%)0.0%
Memory size169.0 KiB
2021-06-19T15:21:47.030190image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1000102
5-th percentile512480335
Q12123049194
median3904930410
Q37308900445
95-th percentile9297300429
Maximum9900000190
Range9899000088
Interquartile range (IQR)5185851251

Descriptive statistics

Standard deviation2876565571
Coefficient of variation (CV)0.6280297396
Kurtosis-1.260541871
Mean4580301521
Median Absolute Deviation (MAD)2402530110
Skewness0.2433285476
Sum9.899405677 × 1013
Variance8.274629486 × 1018
MonotocityNot monotonic
2021-06-19T15:21:47.155392image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7950006203
 
< 0.1%
35230690602
 
< 0.1%
71293045402
 
< 0.1%
26199201702
 
< 0.1%
60215015352
 
< 0.1%
78998000452
 
< 0.1%
66234001872
 
< 0.1%
78564003002
 
< 0.1%
22289002702
 
< 0.1%
75200005202
 
< 0.1%
Other values (21426)21592
99.9%
ValueCountFrequency (%)
10001022
< 0.1%
12000191
< 0.1%
12000211
< 0.1%
28000311
< 0.1%
36000571
< 0.1%
ValueCountFrequency (%)
99000001901
< 0.1%
98950000401
< 0.1%
98423005401
< 0.1%
98423004851
< 0.1%
98423000951
< 0.1%

date
Categorical

HIGH CARDINALITY

Distinct372
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size169.0 KiB
20140623T000000
 
142
20140626T000000
 
131
20140625T000000
 
131
20140708T000000
 
127
20150427T000000
 
126
Other values (367)
20956 

Length

Max length15
Median length15
Mean length15
Min length15

Characters and Unicode

Total characters324195
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)0.1%

Sample

1st row20141013T000000
2nd row20141209T000000
3rd row20150225T000000
4th row20141209T000000
5th row20150218T000000
ValueCountFrequency (%)
20140623T000000142
 
0.7%
20140626T000000131
 
0.6%
20140625T000000131
 
0.6%
20140708T000000127
 
0.6%
20150427T000000126
 
0.6%
20150325T000000123
 
0.6%
20150428T000000121
 
0.6%
20140709T000000121
 
0.6%
20150422T000000121
 
0.6%
20150414T000000121
 
0.6%
Other values (362)20349
94.2%
2021-06-19T15:21:47.397086image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
20140623t000000142
 
0.7%
20140625t000000131
 
0.6%
20140626t000000131
 
0.6%
20140708t000000127
 
0.6%
20150427t000000126
 
0.6%
20150325t000000123
 
0.6%
20150414t000000121
 
0.6%
20140709t000000121
 
0.6%
20150428t000000121
 
0.6%
20150422t000000121
 
0.6%
Other values (362)20349
94.2%

Most occurring characters

ValueCountFrequency (%)
0178543
55.1%
137980
 
11.7%
233852
 
10.4%
T21613
 
6.7%
418999
 
5.9%
511564
 
3.6%
35066
 
1.6%
74354
 
1.3%
64328
 
1.3%
84009
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number302582
93.3%
Uppercase Letter21613
 
6.7%

Most frequent character per category

ValueCountFrequency (%)
0178543
59.0%
137980
 
12.6%
233852
 
11.2%
418999
 
6.3%
511564
 
3.8%
35066
 
1.7%
74354
 
1.4%
64328
 
1.4%
84009
 
1.3%
93887
 
1.3%
ValueCountFrequency (%)
T21613
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common302582
93.3%
Latin21613
 
6.7%

Most frequent character per script

ValueCountFrequency (%)
0178543
59.0%
137980
 
12.6%
233852
 
11.2%
418999
 
6.3%
511564
 
3.8%
35066
 
1.7%
74354
 
1.4%
64328
 
1.4%
84009
 
1.3%
93887
 
1.3%
ValueCountFrequency (%)
T21613
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII324195
100.0%

Most frequent character per block

ValueCountFrequency (%)
0178543
55.1%
137980
 
11.7%
233852
 
10.4%
T21613
 
6.7%
418999
 
5.9%
511564
 
3.6%
35066
 
1.6%
74354
 
1.3%
64328
 
1.3%
84009
 
1.2%

price
Real number (ℝ≥0)

Distinct4028
Distinct (%)18.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean540088.1418
Minimum75000
Maximum7700000
Zeros0
Zeros (%)0.0%
Memory size169.0 KiB
2021-06-19T15:21:47.525251image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum75000
5-th percentile210000
Q1321950
median450000
Q3645000
95-th percentile1156480
Maximum7700000
Range7625000
Interquartile range (IQR)323050

Descriptive statistics

Standard deviation367127.1965
Coefficient of variation (CV)0.6797542255
Kurtosis34.58554043
Mean540088.1418
Median Absolute Deviation (MAD)150000
Skewness4.024069145
Sum1.167292501 × 1010
Variance1.347823784 × 1011
MonotocityNot monotonic
2021-06-19T15:21:47.674631image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
450000172
 
0.8%
350000172
 
0.8%
550000159
 
0.7%
500000152
 
0.7%
425000150
 
0.7%
325000148
 
0.7%
400000145
 
0.7%
375000138
 
0.6%
300000133
 
0.6%
525000131
 
0.6%
Other values (4018)20113
93.1%
ValueCountFrequency (%)
750001
< 0.1%
780001
< 0.1%
800001
< 0.1%
810001
< 0.1%
820001
< 0.1%
ValueCountFrequency (%)
77000001
< 0.1%
70625001
< 0.1%
68850001
< 0.1%
55700001
< 0.1%
53500001
< 0.1%

bedrooms
Real number (ℝ≥0)

Distinct13
Distinct (%)0.1%
Missing4
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean3.370910269
Minimum0
Maximum33
Zeros13
Zeros (%)0.1%
Memory size169.0 KiB
2021-06-19T15:21:47.802471image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median3
Q34
95-th percentile5
Maximum33
Range33
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.9300844679
Coefficient of variation (CV)0.2759149291
Kurtosis49.06741079
Mean3.370910269
Median Absolute Deviation (MAD)1
Skewness1.974439161
Sum72842
Variance0.8650571175
MonotocityNot monotonic
2021-06-19T15:21:47.906759image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
39822
45.4%
46881
31.8%
22759
 
12.8%
51601
 
7.4%
6272
 
1.3%
1199
 
0.9%
738
 
0.2%
813
 
0.1%
013
 
0.1%
96
 
< 0.1%
Other values (3)5
 
< 0.1%
(Missing)4
 
< 0.1%
ValueCountFrequency (%)
013
 
0.1%
1199
 
0.9%
22759
 
12.8%
39822
45.4%
46881
31.8%
ValueCountFrequency (%)
331
 
< 0.1%
111
 
< 0.1%
103
 
< 0.1%
96
< 0.1%
813
0.1%

bathrooms
Real number (ℝ≥0)

Distinct30
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.114757322
Minimum0
Maximum8
Zeros10
Zeros (%)< 0.1%
Memory size169.0 KiB
2021-06-19T15:21:48.024807image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11.75
median2.25
Q32.5
95-th percentile3.5
Maximum8
Range8
Interquartile range (IQR)0.75

Descriptive statistics

Standard deviation0.7701631572
Coefficient of variation (CV)0.3641851238
Kurtosis1.279902444
Mean2.114757322
Median Absolute Deviation (MAD)0.5
Skewness0.5111075733
Sum45706.25
Variance0.5931512887
MonotocityNot monotonic
2021-06-19T15:21:48.155082image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
2.55380
24.9%
13852
17.8%
1.753048
14.1%
2.252047
 
9.5%
21930
 
8.9%
1.51446
 
6.7%
2.751185
 
5.5%
3753
 
3.5%
3.5731
 
3.4%
3.25589
 
2.7%
Other values (20)652
 
3.0%
ValueCountFrequency (%)
010
 
< 0.1%
0.54
 
< 0.1%
0.7572
 
0.3%
13852
17.8%
1.259
 
< 0.1%
ValueCountFrequency (%)
82
< 0.1%
7.751
< 0.1%
7.51
< 0.1%
6.752
< 0.1%
6.52
< 0.1%

sqft_living
Real number (ℝ≥0)

Distinct1038
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2079.899736
Minimum290
Maximum13540
Zeros0
Zeros (%)0.0%
Memory size169.0 KiB
2021-06-19T15:21:48.294698image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum290
5-th percentile940
Q11427
median1910
Q32550
95-th percentile3760
Maximum13540
Range13250
Interquartile range (IQR)1123

Descriptive statistics

Standard deviation918.440897
Coefficient of variation (CV)0.4415794093
Kurtosis5.24309299
Mean2079.899736
Median Absolute Deviation (MAD)540
Skewness1.471555427
Sum44952873
Variance843533.6814
MonotocityNot monotonic
2021-06-19T15:21:48.445041image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1300138
 
0.6%
1400135
 
0.6%
1440133
 
0.6%
1010129
 
0.6%
1660129
 
0.6%
1800129
 
0.6%
1820128
 
0.6%
1720125
 
0.6%
1480125
 
0.6%
1540124
 
0.6%
Other values (1028)20318
94.0%
ValueCountFrequency (%)
2901
< 0.1%
3701
< 0.1%
3801
< 0.1%
3841
< 0.1%
3902
< 0.1%
ValueCountFrequency (%)
135401
< 0.1%
120501
< 0.1%
100401
< 0.1%
98901
< 0.1%
96401
< 0.1%

sqft_lot
Real number (ℝ≥0)

Distinct9782
Distinct (%)45.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15106.96757
Minimum520
Maximum1651359
Zeros0
Zeros (%)0.0%
Memory size169.0 KiB
2021-06-19T15:21:48.568504image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum520
5-th percentile1800
Q15040
median7618
Q310688
95-th percentile43339.2
Maximum1651359
Range1650839
Interquartile range (IQR)5648

Descriptive statistics

Standard deviation41420.51152
Coefficient of variation (CV)2.741815082
Kurtosis285.0778197
Mean15106.96757
Median Absolute Deviation (MAD)2618
Skewness13.06001896
Sum326506890
Variance1715658774
MonotocityNot monotonic
2021-06-19T15:21:48.689951image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5000358
 
1.7%
6000290
 
1.3%
4000251
 
1.2%
7200220
 
1.0%
4800120
 
0.6%
7500119
 
0.6%
4500114
 
0.5%
8400111
 
0.5%
9600109
 
0.5%
3600103
 
0.5%
Other values (9772)19818
91.7%
ValueCountFrequency (%)
5201
< 0.1%
5721
< 0.1%
6001
< 0.1%
6091
< 0.1%
6351
< 0.1%
ValueCountFrequency (%)
16513591
< 0.1%
11647941
< 0.1%
10742181
< 0.1%
10240681
< 0.1%
9829981
< 0.1%

floors
Real number (ℝ≥0)

Distinct6
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1.494331853
Minimum1
Maximum3.5
Zeros0
Zeros (%)0.0%
Memory size169.0 KiB
2021-06-19T15:21:48.788440image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1.5
Q32
95-th percentile2
Maximum3.5
Range2.5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.539990919
Coefficient of variation (CV)0.3613594383
Kurtosis-0.4847811464
Mean1.494331853
Median Absolute Deviation (MAD)0.5
Skewness0.616106725
Sum32295.5
Variance0.2915901926
MonotocityNot monotonic
2021-06-19T15:21:48.873880image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
110679
49.4%
28241
38.1%
1.51910
 
8.8%
3613
 
2.8%
2.5161
 
0.7%
3.58
 
< 0.1%
(Missing)1
 
< 0.1%
ValueCountFrequency (%)
110679
49.4%
1.51910
 
8.8%
28241
38.1%
2.5161
 
0.7%
3613
 
2.8%
ValueCountFrequency (%)
3.58
 
< 0.1%
3613
 
2.8%
2.5161
 
0.7%
28241
38.1%
1.51910
 
8.8%

waterfront
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size169.0 KiB
0
21450 
1
 
163

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21613
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
021450
99.2%
1163
 
0.8%
2021-06-19T15:21:49.062554image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-06-19T15:21:49.130662image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
021450
99.2%
1163
 
0.8%

Most occurring characters

ValueCountFrequency (%)
021450
99.2%
1163
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number21613
100.0%

Most frequent character per category

ValueCountFrequency (%)
021450
99.2%
1163
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common21613
100.0%

Most frequent character per script

ValueCountFrequency (%)
021450
99.2%
1163
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII21613
100.0%

Most frequent character per block

ValueCountFrequency (%)
021450
99.2%
1163
 
0.8%

view
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size169.0 KiB
0
19489 
2
 
963
3
 
510
1
 
332
4
 
319

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21613
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
019489
90.2%
2963
 
4.5%
3510
 
2.4%
1332
 
1.5%
4319
 
1.5%
2021-06-19T15:21:49.329515image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-06-19T15:21:49.399271image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
019489
90.2%
2963
 
4.5%
3510
 
2.4%
1332
 
1.5%
4319
 
1.5%

Most occurring characters

ValueCountFrequency (%)
019489
90.2%
2963
 
4.5%
3510
 
2.4%
1332
 
1.5%
4319
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number21613
100.0%

Most frequent character per category

ValueCountFrequency (%)
019489
90.2%
2963
 
4.5%
3510
 
2.4%
1332
 
1.5%
4319
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
Common21613
100.0%

Most frequent character per script

ValueCountFrequency (%)
019489
90.2%
2963
 
4.5%
3510
 
2.4%
1332
 
1.5%
4319
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII21613
100.0%

Most frequent character per block

ValueCountFrequency (%)
019489
90.2%
2963
 
4.5%
3510
 
2.4%
1332
 
1.5%
4319
 
1.5%

condition
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size169.0 KiB
3
14031 
4
5679 
5
1701 
2
 
172
1
 
30

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21613
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row5
5th row3
ValueCountFrequency (%)
314031
64.9%
45679
26.3%
51701
 
7.9%
2172
 
0.8%
130
 
0.1%
2021-06-19T15:21:49.605405image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-06-19T15:21:49.676208image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
314031
64.9%
45679
26.3%
51701
 
7.9%
2172
 
0.8%
130
 
0.1%

Most occurring characters

ValueCountFrequency (%)
314031
64.9%
45679
26.3%
51701
 
7.9%
2172
 
0.8%
130
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number21613
100.0%

Most frequent character per category

ValueCountFrequency (%)
314031
64.9%
45679
26.3%
51701
 
7.9%
2172
 
0.8%
130
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common21613
100.0%

Most frequent character per script

ValueCountFrequency (%)
314031
64.9%
45679
26.3%
51701
 
7.9%
2172
 
0.8%
130
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII21613
100.0%

Most frequent character per block

ValueCountFrequency (%)
314031
64.9%
45679
26.3%
51701
 
7.9%
2172
 
0.8%
130
 
0.1%

grade
Real number (ℝ≥0)

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.656873178
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Memory size169.0 KiB
2021-06-19T15:21:49.755698image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q17
median7
Q38
95-th percentile10
Maximum13
Range12
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.175458757
Coefficient of variation (CV)0.1535168116
Kurtosis1.190932077
Mean7.656873178
Median Absolute Deviation (MAD)1
Skewness0.7711032008
Sum165488
Variance1.381703289
MonotocityNot monotonic
2021-06-19T15:21:49.860447image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
78981
41.6%
86068
28.1%
92615
 
12.1%
62038
 
9.4%
101134
 
5.2%
11399
 
1.8%
5242
 
1.1%
1290
 
0.4%
429
 
0.1%
1313
 
0.1%
Other values (2)4
 
< 0.1%
ValueCountFrequency (%)
11
 
< 0.1%
33
 
< 0.1%
429
 
0.1%
5242
 
1.1%
62038
9.4%
ValueCountFrequency (%)
1313
 
0.1%
1290
 
0.4%
11399
 
1.8%
101134
5.2%
92615
12.1%

sqft_above
Real number (ℝ≥0)

Distinct946
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1788.390691
Minimum290
Maximum9410
Zeros0
Zeros (%)0.0%
Memory size169.0 KiB
2021-06-19T15:21:49.980714image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum290
5-th percentile850
Q11190
median1560
Q32210
95-th percentile3400
Maximum9410
Range9120
Interquartile range (IQR)1020

Descriptive statistics

Standard deviation828.0909777
Coefficient of variation (CV)0.4630369538
Kurtosis3.402303621
Mean1788.390691
Median Absolute Deviation (MAD)450
Skewness1.446664473
Sum38652488
Variance685734.6673
MonotocityNot monotonic
2021-06-19T15:21:50.099960image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1300212
 
1.0%
1010210
 
1.0%
1200206
 
1.0%
1220192
 
0.9%
1140184
 
0.9%
1400180
 
0.8%
1060178
 
0.8%
1180177
 
0.8%
1340176
 
0.8%
1250174
 
0.8%
Other values (936)19724
91.3%
ValueCountFrequency (%)
2901
< 0.1%
3701
< 0.1%
3801
< 0.1%
3841
< 0.1%
3902
< 0.1%
ValueCountFrequency (%)
94101
< 0.1%
88601
< 0.1%
85701
< 0.1%
80201
< 0.1%
78801
< 0.1%

sqft_basement
Real number (ℝ≥0)

ZEROS

Distinct306
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean291.5090455
Minimum0
Maximum4820
Zeros13126
Zeros (%)60.7%
Memory size169.0 KiB
2021-06-19T15:21:50.215604image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3560
95-th percentile1190
Maximum4820
Range4820
Interquartile range (IQR)560

Descriptive statistics

Standard deviation442.5750427
Coefficient of variation (CV)1.518220616
Kurtosis2.715574211
Mean291.5090455
Median Absolute Deviation (MAD)0
Skewness1.577965056
Sum6300385
Variance195872.6684
MonotocityNot monotonic
2021-06-19T15:21:50.344181image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
013126
60.7%
600221
 
1.0%
700218
 
1.0%
500214
 
1.0%
800206
 
1.0%
400184
 
0.9%
1000149
 
0.7%
900144
 
0.7%
300142
 
0.7%
200108
 
0.5%
Other values (296)6901
31.9%
ValueCountFrequency (%)
013126
60.7%
102
 
< 0.1%
201
 
< 0.1%
404
 
< 0.1%
5011
 
0.1%
ValueCountFrequency (%)
48201
< 0.1%
41301
< 0.1%
35001
< 0.1%
34801
< 0.1%
32601
< 0.1%

yr_built
Real number (ℝ≥0)

Distinct116
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1971.005136
Minimum1900
Maximum2015
Zeros0
Zeros (%)0.0%
Memory size169.0 KiB
2021-06-19T15:21:50.465770image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1900
5-th percentile1915
Q11951
median1975
Q31997
95-th percentile2011
Maximum2015
Range115
Interquartile range (IQR)46

Descriptive statistics

Standard deviation29.3734108
Coefficient of variation (CV)0.01490275711
Kurtosis-0.6574075047
Mean1971.005136
Median Absolute Deviation (MAD)23
Skewness-0.4698053988
Sum42599334
Variance862.7972622
MonotocityNot monotonic
2021-06-19T15:21:50.615035image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2014559
 
2.6%
2006454
 
2.1%
2005450
 
2.1%
2004433
 
2.0%
2003422
 
2.0%
2007417
 
1.9%
1977417
 
1.9%
1978387
 
1.8%
1968381
 
1.8%
2008367
 
1.7%
Other values (106)17326
80.2%
ValueCountFrequency (%)
190087
0.4%
190129
 
0.1%
190227
 
0.1%
190346
0.2%
190445
0.2%
ValueCountFrequency (%)
201538
 
0.2%
2014559
2.6%
2013201
 
0.9%
2012170
 
0.8%
2011130
 
0.6%

yr_renovated
Real number (ℝ≥0)

ZEROS

Distinct70
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.4022579
Minimum0
Maximum2015
Zeros20699
Zeros (%)95.8%
Memory size169.0 KiB
2021-06-19T15:21:50.768754image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum2015
Range2015
Interquartile range (IQR)0

Descriptive statistics

Standard deviation401.67924
Coefficient of variation (CV)4.759105384
Kurtosis18.70115212
Mean84.4022579
Median Absolute Deviation (MAD)0
Skewness4.549493367
Sum1824186
Variance161346.2119
MonotocityNot monotonic
2021-06-19T15:21:50.923887image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
020699
95.8%
201491
 
0.4%
201337
 
0.2%
200336
 
0.2%
200735
 
0.2%
200035
 
0.2%
200535
 
0.2%
200426
 
0.1%
199025
 
0.1%
200624
 
0.1%
Other values (60)570
 
2.6%
ValueCountFrequency (%)
020699
95.8%
19341
 
< 0.1%
19402
 
< 0.1%
19441
 
< 0.1%
19453
 
< 0.1%
ValueCountFrequency (%)
201516
 
0.1%
201491
0.4%
201337
0.2%
201211
 
0.1%
201113
 
0.1%

zipcode
Real number (ℝ≥0)

Distinct70
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98077.9398
Minimum98001
Maximum98199
Zeros0
Zeros (%)0.0%
Memory size169.0 KiB
2021-06-19T15:21:51.083109image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum98001
5-th percentile98004
Q198033
median98065
Q398118
95-th percentile98177
Maximum98199
Range198
Interquartile range (IQR)85

Descriptive statistics

Standard deviation53.50502626
Coefficient of variation (CV)0.0005455357888
Kurtosis-0.8534788732
Mean98077.9398
Median Absolute Deviation (MAD)42
Skewness0.4056612082
Sum2119758513
Variance2862.787835
MonotocityNot monotonic
2021-06-19T15:21:51.670372image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98103602
 
2.8%
98038590
 
2.7%
98115583
 
2.7%
98052574
 
2.7%
98117553
 
2.6%
98042548
 
2.5%
98034545
 
2.5%
98118508
 
2.4%
98023499
 
2.3%
98006498
 
2.3%
Other values (60)16113
74.6%
ValueCountFrequency (%)
98001362
1.7%
98002199
0.9%
98003280
1.3%
98004317
1.5%
98005168
0.8%
ValueCountFrequency (%)
98199317
1.5%
98198280
1.3%
98188136
0.6%
98178262
1.2%
98177255
1.2%

lat
Real number (ℝ≥0)

Distinct5034
Distinct (%)23.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.56005252
Minimum47.1559
Maximum47.7776
Zeros0
Zeros (%)0.0%
Memory size169.0 KiB
2021-06-19T15:21:51.802773image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum47.1559
5-th percentile47.3103
Q147.471
median47.5718
Q347.678
95-th percentile47.74964
Maximum47.7776
Range0.6217
Interquartile range (IQR)0.207

Descriptive statistics

Standard deviation0.1385637102
Coefficient of variation (CV)0.002913447377
Kurtosis-0.6763130016
Mean47.56005252
Median Absolute Deviation (MAD)0.1049
Skewness-0.4852704765
Sum1027915.415
Variance0.0191999018
MonotocityNot monotonic
2021-06-19T15:21:51.925471image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47.684617
 
0.1%
47.662417
 
0.1%
47.549117
 
0.1%
47.532217
 
0.1%
47.695516
 
0.1%
47.671116
 
0.1%
47.688616
 
0.1%
47.664715
 
0.1%
47.690415
 
0.1%
47.684215
 
0.1%
Other values (5024)21452
99.3%
ValueCountFrequency (%)
47.15591
< 0.1%
47.15931
< 0.1%
47.16221
< 0.1%
47.16471
< 0.1%
47.17641
< 0.1%
ValueCountFrequency (%)
47.77763
< 0.1%
47.77753
< 0.1%
47.77741
 
< 0.1%
47.77723
< 0.1%
47.77712
< 0.1%

long
Real number (ℝ)

Distinct752
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-122.2138964
Minimum-122.519
Maximum-121.315
Zeros0
Zeros (%)0.0%
Memory size169.0 KiB
2021-06-19T15:21:52.048959image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum-122.519
5-th percentile-122.387
Q1-122.328
median-122.23
Q3-122.125
95-th percentile-121.979
Maximum-121.315
Range1.204
Interquartile range (IQR)0.203

Descriptive statistics

Standard deviation0.1408283424
Coefficient of variation (CV)-0.001152310388
Kurtosis1.049500887
Mean-122.2138964
Median Absolute Deviation (MAD)0.101
Skewness0.8850529834
Sum-2641408.943
Variance0.01983262202
MonotocityNot monotonic
2021-06-19T15:21:52.202864image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-122.29116
 
0.5%
-122.3111
 
0.5%
-122.362104
 
0.5%
-122.291100
 
0.5%
-122.36399
 
0.5%
-122.37299
 
0.5%
-122.28898
 
0.5%
-122.35796
 
0.4%
-122.28495
 
0.4%
-122.36594
 
0.4%
Other values (742)20601
95.3%
ValueCountFrequency (%)
-122.5191
< 0.1%
-122.5151
< 0.1%
-122.5141
< 0.1%
-122.5121
< 0.1%
-122.5112
< 0.1%
ValueCountFrequency (%)
-121.3152
< 0.1%
-121.3161
< 0.1%
-121.3191
< 0.1%
-121.3211
< 0.1%
-121.3251
< 0.1%

sqft_living15
Real number (ℝ≥0)

Distinct777
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1986.552492
Minimum399
Maximum6210
Zeros0
Zeros (%)0.0%
Memory size169.0 KiB
2021-06-19T15:21:52.354872image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum399
5-th percentile1140
Q11490
median1840
Q32360
95-th percentile3300
Maximum6210
Range5811
Interquartile range (IQR)870

Descriptive statistics

Standard deviation685.3913043
Coefficient of variation (CV)0.3450154512
Kurtosis1.59709581
Mean1986.552492
Median Absolute Deviation (MAD)410
Skewness1.108181276
Sum42935359
Variance469761.2399
MonotocityNot monotonic
2021-06-19T15:21:52.498840image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1540197
 
0.9%
1440195
 
0.9%
1560192
 
0.9%
1500181
 
0.8%
1460169
 
0.8%
1580167
 
0.8%
1610166
 
0.8%
1800166
 
0.8%
1720166
 
0.8%
1620165
 
0.8%
Other values (767)19849
91.8%
ValueCountFrequency (%)
3991
< 0.1%
4602
< 0.1%
6202
< 0.1%
6701
< 0.1%
6902
< 0.1%
ValueCountFrequency (%)
62101
 
< 0.1%
61101
 
< 0.1%
57906
< 0.1%
56101
 
< 0.1%
56001
 
< 0.1%

sqft_lot15
Real number (ℝ≥0)

Distinct8689
Distinct (%)40.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12768.45565
Minimum651
Maximum871200
Zeros0
Zeros (%)0.0%
Memory size169.0 KiB
2021-06-19T15:21:52.655575image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum651
5-th percentile1999.2
Q15100
median7620
Q310083
95-th percentile37062.8
Maximum871200
Range870549
Interquartile range (IQR)4983

Descriptive statistics

Standard deviation27304.17963
Coefficient of variation (CV)2.138408933
Kurtosis150.76311
Mean12768.45565
Median Absolute Deviation (MAD)2505
Skewness9.506743247
Sum275964632
Variance745518225.3
MonotocityNot monotonic
2021-06-19T15:21:52.798844image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5000427
 
2.0%
4000357
 
1.7%
6000289
 
1.3%
7200211
 
1.0%
4800145
 
0.7%
7500142
 
0.7%
8400116
 
0.5%
3600111
 
0.5%
4500111
 
0.5%
5100109
 
0.5%
Other values (8679)19595
90.7%
ValueCountFrequency (%)
6511
 
< 0.1%
6591
 
< 0.1%
6601
 
< 0.1%
7482
< 0.1%
7504
< 0.1%
ValueCountFrequency (%)
8712001
< 0.1%
8581321
< 0.1%
5606171
< 0.1%
4382131
< 0.1%
4347281
< 0.1%

Interactions

2021-06-19T15:21:12.683726image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-19T15:21:12.800033image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-19T15:21:12.903080image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-19T15:21:13.012358image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-19T15:21:13.119659image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-19T15:21:13.320570image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-19T15:21:13.427061image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-19T15:21:13.526118image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-19T15:21:13.626760image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-19T15:21:13.727853image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-19T15:21:13.832804image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-19T15:21:13.940507image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-19T15:21:14.052528image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-19T15:21:14.158828image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-19T15:21:14.262149image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-19T15:21:14.363526image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-19T15:21:14.464444image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-19T15:21:14.574728image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-19T15:21:14.684919image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-19T15:21:14.799615image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-19T15:21:14.912611image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-19T15:21:15.025712image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-19T15:21:15.137515image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-19T15:21:15.247645image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-19T15:21:15.355514image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-19T15:21:15.466943image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-19T15:21:15.580717image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-19T15:21:15.696193image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-19T15:21:15.918885image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-19T15:21:16.036870image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-19T15:21:16.153842image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-19T15:21:16.265054image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-19T15:21:16.374364image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-19T15:21:16.483036image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-19T15:21:16.599522image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-19T15:21:16.717106image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-19T15:21:16.833782image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-19T15:21:16.947261image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-19T15:21:17.049183image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-19T15:21:17.155480image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-19T15:21:17.255693image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-19T15:21:17.360951image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-19T15:21:17.464637image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-19T15:21:17.575583image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-19T15:21:17.684926image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-19T15:21:17.791345image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-19T15:21:17.895427image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-19T15:21:17.999909image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-19T15:21:18.102259image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-19T15:21:18.212218image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-19T15:21:18.325493image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-19T15:21:18.435717image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
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2021-06-19T15:21:43.724221image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-19T15:21:43.832939image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-19T15:21:43.933749image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-19T15:21:44.038237image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-19T15:21:44.148804image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-19T15:21:44.261788image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-19T15:21:44.383730image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-19T15:21:44.502006image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-19T15:21:44.621557image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-19T15:21:44.736557image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-19T15:21:44.850602image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-19T15:21:44.960692image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-19T15:21:45.074849image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-19T15:21:45.194578image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-19T15:21:45.306386image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-19T15:21:45.415959image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-19T15:21:45.520538image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-19T15:21:45.620856image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-19T15:21:45.719179image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Correlations

2021-06-19T15:21:52.957991image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-06-19T15:21:53.192894image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-06-19T15:21:53.402913image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-06-19T15:21:53.611190image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-06-19T15:21:53.801785image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-06-19T15:21:45.986550image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
A simple visualization of nullity by column.
2021-06-19T15:21:46.453706image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-06-19T15:21:46.684415image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-06-19T15:21:46.804303image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

iddatepricebedroomsbathroomssqft_livingsqft_lotfloorswaterfrontviewconditiongradesqft_abovesqft_basementyr_builtyr_renovatedzipcodelatlongsqft_living15sqft_lot15
0712930052020141013T000000221900.03.01.00118056501.0003711800195509817847.5112-122.25713405650
1641410019220141209T000000538000.03.02.25257072422.000372170400195119919812547.7210-122.31916907639
2563150040020150225T000000180000.02.01.00770100001.000367700193309802847.7379-122.23327208062
3248720087520141209T000000604000.04.03.00196050001.000571050910196509813647.5208-122.39313605000
4195440051020150218T000000510000.03.02.00168080801.0003816800198709807447.6168-122.04518007503
5723755031020140512T0000001225000.04.04.5054201019301.00031138901530200109805347.6561-122.0054760101930
6132140006020140627T000000257500.03.02.25171568192.0003717150199509800347.3097-122.32722386819
7200800027020150115T000000291850.03.01.50106097111.0003710600196309819847.4095-122.31516509711
8241460012620150415T000000229500.03.01.00178074701.000371050730196009814647.5123-122.33717808113
9379350016020150312T000000323000.03.02.50189065602.0003718900200309803847.3684-122.03123907570

Last rows

iddatepricebedroomsbathroomssqft_livingsqft_lotfloorswaterfrontviewconditiongradesqft_abovesqft_basementyr_builtyr_renovatedzipcodelatlongsqft_living15sqft_lot15
21603785214004020140825T000000507250.03.02.50227055362.0003822700200309806547.5389-121.88122705731
21604983420136720150126T000000429000.03.02.00149011263.0003814900201409814447.5699-122.28814001230
21605344890021020141014T000000610685.04.02.50252060232.0003925200201409805647.5137-122.16725206023
21606793600042920150326T0000001007500.04.03.50351072002.000392600910200909813647.5537-122.39820506200
21607299780002120150219T000000475000.03.02.50131012942.000381180130200809811647.5773-122.40913301265
2160826300001820140521T000000360000.03.02.50153011313.0003815300200909810347.6993-122.34615301509
21609660006012020150223T000000400000.04.02.50231058132.0003823100201409814647.5107-122.36218307200
21610152330014120140623T000000402101.02.00.75102013502.0003710200200909814447.5944-122.29910202007
2161129131010020150116T000000400000.03.02.50160023882.0003816000200409802747.5345-122.06914101287
21612152330015720141015T000000325000.02.00.75102010762.0003710200200809814447.5941-122.29910201357